Summary of Boundary-guided Learning For Gene Expression Prediction in Spatial Transcriptomics, by Mingcheng Qu et al.
Boundary-Guided Learning for Gene Expression Prediction in Spatial Transcriptomics
by Mingcheng Qu, Yuncong Wu, Donglin Di, Anyang Su, Tonghua Su, Yang Song, Lei Fan
First submitted to arxiv on: 5 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel framework called BG-TRIPLEX to predict gene expression from whole-slide images (WSIs) using spatial transcriptomics (ST) data. The model leverages boundary information from pathological images as guiding features to enhance prediction accuracy. It consists of three branches: spot, in-context, and global, which extract boundary features, cellular morphology, and microenvironment characteristics, respectively. These features are then integrated to predict the final output. The authors conducted extensive experiments on three public ST datasets, demonstrating that BG-TRIPLEX consistently outperforms existing methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to guess what genes are doing in cells by looking at pictures of those cells. It uses information from special tissue samples called spatial transcriptomics data. The researchers created a special computer program called BG-TRIPLEX that can use this information to make better predictions about which genes are turned on or off in different parts of the cell. They tested it with real data and showed that it’s more accurate than other methods. |
